Explanation-based Ranking in Opinionated Recommender Systems

Explanations can help people to make better choices, but their use in recommender systems has so far been limited to the annotation of recommendations after they have been ranked and suggested to the user. In this paper we argue that explanations can also be used to rank recommendations. We describe a technique that uses the strength of an item’s explanation as a ranking signal – preferring items with compelling explanations – and demonstrate its efficacy on a real-world dataset.